Artificial neural network for ecg classification - Jaiswal, Gaurav Kumar; Paul, Ranbir (Recent Research in Science and Technology, 2014)
<!–[if gte mso 9]> Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 <![endif]–> This research work is supervised by ANN based algorithm to classify the ECG waveforms. The ECG waveform gives the almost all information about activity of the heart, which is depending on the electrical activity of the heart. In this paper we are focused only five features of ECG signal P, Q, R, S, T. This is achieved by extracting the various features and duration of ECG waveform P-wave, PR segment, PR interval, QRS Complex, ST segment, T-wave, ST- interval, QTc and QRS voltage. ECG signal and heart rate are used the parameter for detection diseases, most of the data comes from PhysioDataNet and MIT-BIH data base. This research is focused on to find out best neural network structure which classifies the abnormalities of heart diseases. This technique also identifies the normal region for classification of abnormalities; because of ECG waveform is varying from person to person at different condition. <!–[if gte mso 9]> <![endif]–><!–[if gte mso 10]> <![endif]–>